Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude 3 Haiku and K-Exaone finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
K-Exaone is the reasoning model in the pair, while Claude 3 Haiku is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. K-Exaone gives you the larger context window at 256K, compared with 200K for Claude 3 Haiku.
Treat this as a split decision. Claude 3 Haiku makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; K-Exaone is the better fit if coding is the priority or you need the larger 256K context window.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Claude 3 Haiku
18.9
K-Exaone
49.4
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Claude 3 Haiku and K-Exaone are tied on overall score, so the right pick depends on which category matters most for your use case.
K-Exaone has the edge for coding in this comparison, averaging 49.4 versus 18.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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